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spaCy
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spaCy/spacy/cli/train.py

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# coding: utf8
from __future__ import unicode_literals, division, print_function
import plac
from pathlib import Path
import tqdm
from thinc.neural._classes.model import Model
from timeit import default_timer as timer
import shutil
from wasabi import Printer
from ._messages import Messages
from .._ml import create_default_optimizer
from ..attrs import PROB, IS_OOV, CLUSTER, LANG
from ..gold import GoldCorpus
from .. import util
from .. import about
# Take dropout and batch size as generators of values -- dropout
# starts high and decays sharply, to force the optimizer to explore.
# Batch size starts at 1 and grows, so that we make updates quickly
# at the beginning of training.
dropout_rates = util.decaying(
util.env_opt("dropout_from", 0.1),
util.env_opt("dropout_to", 0.1),
util.env_opt("dropout_decay", 0.0),
)
batch_sizes = util.compounding(
util.env_opt("batch_from", 750),
util.env_opt("batch_to", 750),
util.env_opt("batch_compound", 1.001),
)
@plac.annotations(
lang=("Model language", "positional", None, str),
output_path=("Output directory to store model in", "positional", None, Path),
train_path=("Location of JSON-formatted training data", "positional", None, Path),
dev_path=("Location of JSON-formatted development data", "positional", None, Path),
base_model=("Name of model to update (optional)", "option", "b", str),
pipeline=("Comma-separated names of pipeline components", "option", "p", str),
vectors=("Model to load vectors from", "option", "v", str),
n_iter=("Number of iterations", "option", "n", int),
n_examples=("Number of examples", "option", "ns", int),
use_gpu=("Use GPU", "option", "g", int),
version=("Model version", "option", "V", str),
meta_path=("Optional path to meta.json to use as base.", "option", "m", Path),
init_tok2vec=(
"Path to pretrained weights for the token-to-vector parts of the models. See 'spacy pretrain'. Experimental.",
"option",
"t2v",
Path,
),
parser_multitasks=(
"Side objectives for parser CNN, e.g. 'dep' or 'dep,tag'",
"option",
"pt",
str,
),
entity_multitasks=(
"Side objectives for NER CNN, e.g. 'dep' or 'dep,tag'",
"option",
"et",
str,
),
noise_level=("Amount of corruption for data augmentation", "option", "nl", float),
gold_preproc=("Use gold preprocessing", "flag", "G", bool),
learn_tokens=("Make parser learn gold-standard tokenization", "flag", "T", bool),
verbose=("Display more information for debug", "flag", "VV", bool),
debug=("Run data diagnostics before training", "flag", "D", bool),
)
def train(
lang,
output_path,
train_path,
dev_path,
base_model=None,
pipeline="tagger,parser,ner",
vectors=None,
n_iter=30,
n_examples=0,
use_gpu=-1,
version="0.0.0",
meta_path=None,
init_tok2vec=None,
parser_multitasks="",
entity_multitasks="",
noise_level=0.0,
gold_preproc=False,
learn_tokens=False,
verbose=False,
debug=False,
):
"""
Train or update a spaCy model. Requires data to be formatted in spaCy's
JSON format. To convert data from other formats, use the `spacy convert`
command.
"""
msg = Printer()
util.fix_random_seed()
util.set_env_log(verbose)
# Make sure all files and paths exists if they are needed
train_path = util.ensure_path(train_path)
dev_path = util.ensure_path(dev_path)
meta_path = util.ensure_path(meta_path)
if not train_path or not train_path.exists():
msg.fail(Messages.M050, train_path, exits=1)
if not dev_path or not dev_path.exists():
msg.fail(Messages.M051, dev_path, exits=1)
if meta_path is not None and not meta_path.exists():
msg.fail(Messages.M020, meta_path, exits=1)
meta = util.read_json(meta_path) if meta_path else {}
if not isinstance(meta, dict):
msg.fail(Messages.M052, Messages.M053.format(meta_type=type(meta)), exits=1)
if output_path.exists() and [p for p in output_path.iterdir() if p.is_dir()]:
msg.fail(Messages.M062, Messages.M065)
if not output_path.exists():
output_path.mkdir()
# Set up the base model and pipeline. If a base model is specified, load
# the model and make sure the pipeline matches the pipeline setting. If
# training starts from a blank model, intitalize the language class.
pipeline = [p.strip() for p in pipeline.split(",")]
msg.text(Messages.M055.format(pipeline=pipeline))
if base_model:
msg.text(Messages.M056.format(model=base_model))
nlp = util.load_model(base_model)
if nlp.lang != lang:
msg.fail(Messages.M072.format(model_lang=nlp.lang, lang=lang), exits=1)
other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipeline]
nlp.disable_pipes(*other_pipes)
for pipe in pipeline:
if pipe not in nlp.pipe_names:
nlp.add_pipe(nlp.create_pipe(pipe))
else:
msg.text(Messages.M057.format(model=lang))
lang_cls = util.get_lang_class(lang)
nlp = lang_cls()
for pipe in pipeline:
nlp.add_pipe(nlp.create_pipe(pipe))
if learn_tokens:
nlp.add_pipe(nlp.create_pipe("merge_subtokens"))
if vectors:
msg.text(Messages.M058.format(model=vectors))
_load_vectors(nlp, vectors)
# Multitask objectives
multitask_options = [("parser", parser_multitasks), ("ner", entity_multitasks)]
for pipe_name, multitasks in multitask_options:
if multitasks:
if pipe_name not in pipeline:
msg.fail(Messages.M059.format(pipe=pipe_name))
pipe = nlp.get_pipe(pipe_name)
for objective in multitasks.split(","):
pipe.add_multitask_objective(objective)
# Prepare training corpus
msg.text(Messages.M060.format(limit=n_examples))
corpus = GoldCorpus(train_path, dev_path, limit=n_examples)
n_train_words = corpus.count_train()
if base_model:
# Start with an existing model, use default optimizer
optimizer = create_default_optimizer(Model.ops)
else:
# Start with a blank model, call begin_training
optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
nlp._optimizer = None
# Load in pre-trained weights
if init_tok2vec is not None:
components = _load_pretrained_tok2vec(nlp, init_tok2vec)
msg.text(Messages.M071.format(components=components))
print(
"\nItn. Dep Loss NER Loss UAS NER P. NER R. NER F. Tag % Token % CPU WPS GPU WPS"
)
try:
for i in range(n_iter):
train_docs = corpus.train_docs(
nlp, noise_level=noise_level, gold_preproc=gold_preproc, max_length=0
)
words_seen = 0
with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
losses = {}
for batch in util.minibatch_by_words(train_docs, size=batch_sizes):
if not batch:
continue
docs, golds = zip(*batch)
nlp.update(
docs,
golds,
sgd=optimizer,
drop=next(dropout_rates),
losses=losses,
)
pbar.update(sum(len(doc) for doc in docs))
words_seen += sum(len(doc) for doc in docs)
with nlp.use_params(optimizer.averages):
util.set_env_log(False)
epoch_model_path = output_path / ("model%d" % i)
nlp.to_disk(epoch_model_path)
nlp_loaded = util.load_model_from_path(epoch_model_path)
dev_docs = list(corpus.dev_docs(nlp_loaded, gold_preproc=gold_preproc))
nwords = sum(len(doc_gold[0]) for doc_gold in dev_docs)
start_time = timer()
scorer = nlp_loaded.evaluate(dev_docs, debug)
end_time = timer()
if use_gpu < 0:
gpu_wps = None
cpu_wps = nwords / (end_time - start_time)
else:
gpu_wps = nwords / (end_time - start_time)
with Model.use_device("cpu"):
nlp_loaded = util.load_model_from_path(epoch_model_path)
dev_docs = list(
corpus.dev_docs(nlp_loaded, gold_preproc=gold_preproc)
)
start_time = timer()
scorer = nlp_loaded.evaluate(dev_docs)
end_time = timer()
cpu_wps = nwords / (end_time - start_time)
acc_loc = output_path / ("model%d" % i) / "accuracy.json"
util.write_json(acc_loc, scorer.scores)
# Update model meta.json
meta["lang"] = nlp.lang
meta["pipeline"] = nlp.pipe_names
meta["spacy_version"] = ">=%s" % about.__version__
meta["accuracy"] = scorer.scores
meta["speed"] = {"nwords": nwords, "cpu": cpu_wps, "gpu": gpu_wps}
meta["vectors"] = {
"width": nlp.vocab.vectors_length,
"vectors": len(nlp.vocab.vectors),
"keys": nlp.vocab.vectors.n_keys,
}
meta.setdefault("name", "model%d" % i)
meta.setdefault("version", version)
meta_loc = output_path / ("model%d" % i) / "meta.json"
util.write_json(meta_loc, meta)
util.set_env_log(verbose)
print_progress(i, losses, scorer.scores, cpu_wps=cpu_wps, gpu_wps=gpu_wps)
finally:
with msg.loading(Messages.M061):
with nlp.use_params(optimizer.averages):
final_model_path = output_path / "model-final"
nlp.to_disk(final_model_path)
msg.good(Messages.M066, util.path2str(final_model_path))
_collate_best_model(meta, output_path, nlp.pipe_names)
def _load_vectors(nlp, vectors):
util.load_model(vectors, vocab=nlp.vocab)
for lex in nlp.vocab:
values = {}
for attr, func in nlp.vocab.lex_attr_getters.items():
# These attrs are expected to be set by data. Others should
# be set by calling the language functions.
if attr not in (CLUSTER, PROB, IS_OOV, LANG):
values[lex.vocab.strings[attr]] = func(lex.orth_)
lex.set_attrs(**values)
lex.is_oov = False
def _load_pretrained_tok2vec(nlp, loc):
"""Load pre-trained weights for the 'token-to-vector' part of the component
models, which is typically a CNN. See 'spacy pretrain'. Experimental.
"""
with loc.open("rb") as file_:
weights_data = file_.read()
loaded = []
for name, component in nlp.pipeline:
if hasattr(component, "model") and hasattr(component.model, "tok2vec"):
component.tok2vec.from_bytes(weights_data)
loaded.append(name)
return loaded
def _collate_best_model(meta, output_path, components):
bests = {}
for component in components:
bests[component] = _find_best(output_path, component)
best_dest = output_path / "model-best"
shutil.copytree(output_path / "model-final", best_dest)
for component, best_component_src in bests.items():
shutil.rmtree(best_dest / component)
shutil.copytree(best_component_src / component, best_dest / component)
accs = util.read_json(best_component_src / "accuracy.json")
for metric in _get_metrics(component):
meta["accuracy"][metric] = accs[metric]
util.write_json(best_dest / "meta.json", meta)
def _find_best(experiment_dir, component):
accuracies = []
for epoch_model in experiment_dir.iterdir():
if epoch_model.is_dir() and epoch_model.parts[-1] != "model-final":
accs = util.read_json(epoch_model / "accuracy.json")
scores = [accs.get(metric, 0.0) for metric in _get_metrics(component)]
accuracies.append((scores, epoch_model))
if accuracies:
return max(accuracies)[1]
else:
return None
def _get_metrics(component):
if component == "parser":
return ("las", "uas", "token_acc")
elif component == "tagger":
return ("tags_acc",)
elif component == "ner":
return ("ents_f", "ents_p", "ents_r")
return ("token_acc",)
def print_progress(itn, losses, dev_scores, cpu_wps=0.0, gpu_wps=0.0):
scores = {}
for col in [
"dep_loss",
"tag_loss",
"uas",
"tags_acc",
"token_acc",
"ents_p",
"ents_r",
"ents_f",
"cpu_wps",
"gpu_wps",
]:
scores[col] = 0.0
scores["dep_loss"] = losses.get("parser", 0.0)
scores["ner_loss"] = losses.get("ner", 0.0)
scores["tag_loss"] = losses.get("tagger", 0.0)
scores.update(dev_scores)
scores["cpu_wps"] = cpu_wps
scores["gpu_wps"] = gpu_wps or 0.0
tpl = "".join(
(
"{:<6d}",
"{dep_loss:<10.3f}",
"{ner_loss:<10.3f}",
"{uas:<8.3f}",
"{ents_p:<8.3f}",
"{ents_r:<8.3f}",
"{ents_f:<8.3f}",
"{tags_acc:<8.3f}",
"{token_acc:<9.3f}",
"{cpu_wps:<9.1f}",
"{gpu_wps:.1f}",
)
)
print(tpl.format(itn, **scores))
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